Suchergebnisse

This is a preprint of the paper: Benítez-Guijarro A. et al. (2021) Co-creating Requirements and Assessing End-User Acceptability of a Voice-Based Chatbot to Support Mental Health: A Thematic Analysis of a Living Lab Workshop. In: D'Haro L.F., Callejas Z., Nakamura S. (eds) Conversational Dialogue Systems for the Next Decade. Lecture Notes in Electrical Engineering, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-15-8395-7_15 This preprint follows Springer Self-archiving policy for non-open access books and chapters (https://www.springer. com/gp/open-access/publication-policies/self-archiving-policy): "authors may deposit a portion of the pre-submission version of their manuscript (preprint) in a recognised preprint server (.). Thi ; Mental health and mental wellbeing have become an important factor to many citizens navigating their way through their environment and in the work place. New technology solutions such as chatbots are potential channels for supporting and coaching users to maintain a good state of mental wellbeing. Chatbots have the added value of providing social conversations and coaching 24/7 outside from conventional mental health services. However, little is known about the acceptability and user led requirements of this technology. This paper uses a living lab approach to elicit requirements, opinions and attitudes towards the use of chatbots for supporting mental health. The data collected was acquired from people living with anxiety or mild depression in a workshop setting. The audio of the workshop was recorded and a thematic analysis was carried out. The results are the co-created functional requirements and a number of use case scenarios that can be of interest to guide future development of chatbots in the mental health domain. ; This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 823907 (MENHIR: Mental health monitoring through interactive conversations https://menhir-project.eu).

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM. ; This publication is based upon work from COST Action Open Multiscale Systems Medicine (OpenMultiMed, CA15120), supported by COST (European Cooperation in Science and Technology). COST is funded by the Horizon 2020 Framework Programme of the European Union. HZ and HYW are also supported by the MetaPlat(690998), SenseCare(690862) and STOP(823978) projects funded by H2020 RISE programme. FC and PT acknowledge the support of H2020 project iPC "individualized Paediatric Cure" (826121). Participation of V.S. in OpenMultiMed is supported by the Czech Ministry of Education, Youth and Sports (project LTC18074). JLM. thanks Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre (ESTG/IPP); and Centro de Recursos Naturais e Ambiente, Instituto Superior Técnico (CERENA/IST) within the support of FCT-Fundação para a Ciência e a Tecnologia through the strategic project FCT-UIDB/04028/2020. MZ acknowledges the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711) and the funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (851255). The Northern Ireland Centre for Stratified Medicine has been financed by a grant awarded to AJ Bjourson under the European Union Regional Development Fund (ERDF) EU Sustainable Competitiveness Programme for Northern Ireland & the Northern Ireland Public Health Agency (HSC R&D). TSR also acknowledges funding from PHA R&D Division and the Western Health & Social Care. ; Peer reviewed